What is Agentic AI?

You have heard it thrown around like a buzzword, let’s talk about what it does and how it works.

AI automation systems have evolved past the point of learning how to generate information based on what it has been fed. The systems can now think independently and proactively make decisions using the tools at their disposal. 

Agentic AI is officially defined as an artificial intelligence system that can complete tasks with little to no supervision using AI agents that mimic human decision-making abilities to solve real-time problems. There can be more than one agent in a system that accomplishes a specific, smaller task in a coordinated effort to reach a larger shared goal. This is known as AI orchestration. 

Advantages of Agentic AI systems 

With further developments in AI, there are always fears that linger about the full potential of these systems malfunctioning in an unpredictable and unanticipated manner later in society; however, there are more positives to the usage and progression of Agentic AI systems that possibly outweigh the negatives.

For one, the biggest and most prominent/best advancement in agentic AI is that it operates autonomously, performing tasks with little consistent human oversight. They can manage long-term goals, problem-solving tasks that include many steps, and track their progress over time. The agentic systems are also proactive, with the flexibility of LLMs, where they can generate a response based on nuanced and context-dependent features that traditional AI systems possess. This allows them to ‘think’ in a more human-like fashion. 

The agents can also specialize in specific tasks, where some agents focus on one simple repetitive task while others make use of perception and memory to solve more complex problems. Within agentic architecture, there are conductors that oversee the progression of tasks and supervise other simpler agents, which is ideal for workflows that follow sequences prone to bottlenecks. Some architects are horizontal, meaning that they work as collaborators in a decentralized manner (although making them slower) as opposed to functioning in a hierarchy. The different applications function according to the different architectural demands. Lastly, Agentic AI is adaptable and intuitive. Agents can learn from their experiences, take in feedback and make adjustments to their behaviour and with the right guidance, can improve themselves continuously. Agents are powered by LLMs, meaning that they can be engaged with by users using natural prompts. This allows them to replace entire software interfaces with simple language or voice commands. 

So, how does Agentic AI work?

Agentic AI tools take on the form of tools suited to solve a problem or complete a task. It begins by creating perception by collecting data about its environment using sensors, APIs, databases or user interactions as a way of ensuring that it is up to date with the information it wants to act upon. 

With the data collected, the AI collects any meaningful insights using natural learning processing (NLPs), computer vision and interprets user queries and patterns to understand the broader context, formulating reasoning to determine the best course of action to take given the task. 

It then sets goals based on predefined or user inputs to create a strategy to achieve that goal using decision trees, reinforcement learning, etc. 

Using the goals and information that it has, the AI then makes decisions by evaluating the multiple actions it can take and choosing the best, most optimal one based on efficiency, accuracy and predicted outcomes.

The AI then executes after selecting an option and interacts with external systems such as APIs, data and robots, or responds to users. 

After executing the chosen action, it learns and adapts by evaluating the outcomes of the decision taken based on collected feedback to improve its future decisions, refining its strategies over time. 

Lastly, the AI orchestrates the coordination and management of its agents and systems. Platforms that orchestrate and automate AI workflows, track their progress towards completing a task, manage the use of resources and handle events where failure occurs. 

The right AI architecture allows for the harmonious productivity of dozens or even thousands of agents working together.